Tracking technologies are able to provide high-resolution movement data that could advance research in different fields, such as tourism management. In this specific field, developing methods to extract moving flock patterns from these datasets is particularly relevant to enable us to improve our knowledge of visitors' behaviour and their impact on a tourist destination (e.g. recreational park or a historical city). Understanding moving flock patterns in tourist destinations is crucial for a good management of attractions and for designing sustainable development policies. However, 'flocking' has been usually associated with the form of collective movement of a large group of birds, fish, insects, and certain mammals as well. Very few research efforts have been devoted in detecting flock patterns associated with pedestrian movement. In the present work, we propose a moving flock pattern definition and a corresponding detection algorithm based on the notion of collective coherence. We use the term collective coherence to refer to behaviour that is exhibited by a group of visitors in a tourist destination. If there is a pedestrian movement to be regarded as a moving flock, then we can at least say all flock members are moving together for a certain period of time) and also exhibiting a form of interaction (e.g. path following). Furthermore, we evaluate the proposed algorithm by applying it to two different pedestrian movement datasets, which have been gathered from visitors of two recreational parks. The results show that the algorithm is capable of detecting moving flock patterns, disqualifying the patterns with flock members that remain stationary in a common place during the considered time interval.